This invention relates to a method and system for processing medical image data to aid in the detection and diagnosis of disease, and more particularly, to a method and system for detecting lung disease in medical images obtained from a x-ray computed tomography (CT) system.
A x-ray chest radiograph system is the more commonly used diagnostic tool useful for the purpose of detecting lung disease in humans. Lung disease such as bronchitis, emphesema and lung cancer are also detectable in chest radiographs and CT. However, CT systems generally provide over 80 separate images for a single CT scan thereby providing a considerable amount of information to a radiologist for use in interpreting the images and detecting suspect regions that may indicate disease.
Suspect regions are defined as those regions a trained radiologist would recommend following through subsequent diagnostic imaging, biopsy, functional lung testing, or other methods. The considerable volume of data presented by a single CT scan presents a time-consuming process for radiologists. Conventional lung cancer screening generally involves a manual interpretation of the 80 or more images by the radiologist. Fatigue is therefore a significant factor affecting sensitivity and specificity of the human reading. In other diseases, such as emphysema, it is difficult for a radiologist to classify the extent of disease progression by only looking at the CT images. Quantitative analysis of the anatomy is required.
Attempts to automate lung cancer and emphysema detection in CT scans have been based on a variety of nodule detection and classification techniques, and lung parenchyma metrics. The emerging field is referred to as Computer Aided Diagnosis, or alternatively, Computer Aided Detection (CAD). There is a significant amount of literature on methods for automating lung cancer detection in CT scans. Generally nodule detection has proceeded in three steps: lung segmentation, vessel extraction, and final nodule candidate detection and classification.
Vessel extraction has been attempted using gray-level thresholding, fuzzy clustering, and three-dimensional seeded region growing). Nodule detection has been done using template matching, genetic algorithms, gray-level thresholding, the N-Quoit filter, region growing, and edge-gradient techniques.
Once candidate nodules are produced by any of the above methods, classification has been implemented via rule-based methods, neural network classification, fuzzy logic, and statistical techniques including factor analysis and linear discriminating analysis.
The above techniques presented to date, however, have largely focused on identifying suspicious lesions in CT scans and have not directly addressed obtaining correct differentiation of structures in the lung and correct measurements of their size. Additionally, the above techniques are generally limited in the interpretative nature of the results. Typically, identification and classification of a lesion using the above techniques may produce a positive affirmation of a nodule, but further radiologist qualitative review and interpretation of results is generally required. For example, radiologists rely heavily on their familiarity with or expert knowledge of pathological and anatomical characteristics of various abnormal and normal structures in interpreting medical images. Further, the characteristics of the scanning device, such as type, pixel intensity and signal impulse response, also influence the presentation of the image data. A radiologist's interpretation of medical images also generally relies on his or her familiarity with a given scanner. There has been no apparent evaluation by the above techniques to address the type of or characteristics of the scanning device in the analysis of the images produced.
What is needed is a robust method and system for processing image data to produce quantitative data to be used in detecting disease. What is further needed is a method and system that provides interpretative results based on expert knowledge of a disease as well as the scanner capabilities and characteristics. Additionally, there is a requirement for the ability to track a disease's progression/regression resulting from drug therapy.